Telecommunications networks increasingly incorporate abilities to self-configure, self-organise and self-adapt. As the size and complexity of telecommunications networks increases, there is a drive to implement these so-called “self-x” properties in a decentralised manner, namely where each node can act individually using only local information.
Accordingly, there is a growing need to develop self-x algorithms, i.e. algorithms for network node self-adaptation, that have to work without global information about the network nor coordinated central control of the network nodes.
A known approach is for self-x algorithms to be designed by skilled engineers, based on specific assumptions about the network that may not be realistic, so there is often a need for the algorithms to be evaluated, revised and refined after they have been implemented in networks. This can be a slow and expensive process.
In this known approach, it is difficult for the skilled engineer designing algorithms that take account of the various different environments that the nodes will be deployed in. The algorithms are designed based on specific assumptions about the network that often do not hold true in the real world.
In this known approach, a self-x algorithm is designed manually and then the same algorithm is applied across all of the network nodes of a certain type, for example all of the base stations in a wireless cellular network. As there are large differences in operating environments of the nodes, performance is degraded because generally-applicable optimisation algorithms perform less well than algorithms that are more specialised to a particular problem or operating environment. By the way, conversely, specialised algorithms perform less well when applied outside the particular area to which they are specialised.
It is desired to provide improved algorithms.